Synthesized voice authentication engine

ABSTRACT

A system for creating a synthetic voice identifier may include a plurality of synthesized voice authorization (SVA) devices and a biometric combinatory device (BCD). The SVAs may be communicatively coupled to the BCD via a network and may communicate utilizing a markup language. The SVA devices may capture an audio signal of a voice of a user, modify the audio signal with a randomized audio frequency signal to generate a modified audio signal, and communicate, the modified audio signal as a synthesized voice signal associated with the user. The BCD may receive biometric information corresponding to a user, the biometric information comprising at least audio information associated with a voice of the user, receive, at an integration module, location information corresponding to a location of the user, combine, the location information and audio signal information associated with the user to generate a synthesized voice identifier associated with the user, and communicate the synthesized voice identifier to a remote device for use in an authentication process of the user.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is continuation of and claims priority to U.S.application Ser. No. 15/382,935, filed Dec. 19, 2016, and entitled“Synthesized Voice Authentication Engine,” which is incorporated herebyby referenced in its entirety.

FIELD OF THE INVENTION

Various aspects of the disclosure relate to voice authentication. Morespecifically, aspects of the disclosure relate to systems and methodsfor providing a synthesized voice signal for use for use by individualsin voice authentication for accessing secure locations and/or secureinformation. Additionally, this disclosure relates to analyzing voiceand other biometric information, leveraging information determinedthrough the data analysis to provide more secure authentication ofusers.

BACKGROUND OF THE INVENTION

Different systems and methods have been used to ensure properauthentication of individuals to prevent unauthorized access to securedphysical locations and secured information storage locations. In somecases, different voice authentication techniques may be used toauthenticate an individual attempting to access the secured location orinformation. In many cases, current technologies for securing locationsand/or information mainly focus on use of character strings (e.g., asequence of numbers, a sequence of alphanumeric characters, and thelike), traditional voice authentication techniques, or a combination ofboth. However, traditional voice authentication techniques may not beeffective for individuals with disabilities, or even individualsspeaking a language foreign to the local geographic area in which thevoice authentication security techniques have been implemented.Additionally, traditional voice authentication techniques may beovercome by use of a mimicked vocal pattern, a recorded vocal signalreciting an authorized pass code, and the like. As such, a need has beenrecognized to improve systems and methods for voice authentication thatmay be used by an organization to prevent access to secured locations orinformation by unauthorized individuals, leveraging voice and otherbiometric information.

SUMMARY OF THE INVENTION

Aspects of the disclosure provide effective, efficient, scalable, andconvenient technical solutions that address and overcome the technicalproblems associated with voice authentication systems and methods usedfor preventing unauthorized access to physical locations and/or securedinformation. In particular, one or more aspects of the disclosureprovide systems and techniques for managing access to data via asynthesized voice engine that provides a synthesized voice for use byindividuals in voice authentication applications and a biometriccombinatory device to combine biometric information with voice data androtating frequencies to provide a personalized and secure voice profile.

A system for creating a synthetic voice identifier may include aplurality of synthesized voice authorization (SVA) devices and abiometric combinatory device (BCD). The SVAs may be communicativelycoupled to the BCD via a network and may communicate utilizing a markuplanguage. The SVA devices may capture an audio signal of a voice of auser, modify the audio signal with a randomized audio frequency signalto generate a modified audio signal, and communicate, the modified audiosignal as a synthesized voice signal associated with the user. The BCDmay receive biometric information corresponding to a user, the biometricinformation comprising at least audio information associated with avoice of the user, receive, at an integration module, locationinformation corresponding to a location of the user, combine, thelocation information and audio signal information associated with theuser to generate a synthesized voice identifier associated with theuser, and communicate the synthesized voice identifier to a remotedevice for use in an authentication process of the user.

These features, along with many others, are discussed in greater detailbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example and not limitedin the accompanying figures in which like reference numerals indicatesimilar elements and in which:

FIG. 1 shows an illustrative computing environment corresponding to asynthesized voice authentication system according to aspects of thisdisclosure;

FIG. 2 shows an illustrative synthesized voice authentication (SVA)device according to aspects of this disclosure;

FIG. 3 shows an illustrative biometric combinatory device according toaspects of this disclosure;

FIG. 4 shows an illustrative method for generating a synthesized voiceauthentication signal according to aspects of this disclosure; and

FIG. 5 shows an illustrative method for aggregating and analyzingbiometric and synthesized voice authentication signals according toaspects of this disclosure.

DETAILED DESCRIPTION

In the following description of various illustrative embodiments,reference is made to the accompanying drawings, which form a parthereof, and in which is shown, by way of illustration, variousembodiments in which aspects of the disclosure may be practiced. It isto be understood that other embodiments may be utilized, and structuraland functional modifications may be made, without departing from thescope of the present disclosure.

It is noted that various connections between elements are discussed inthe following description. It is noted that these connections aregeneral and, unless specified otherwise, may be direct or indirect,wired or wireless, and that the specification is not intended to belimiting in this respect.

FIG. 1 shows an illustrative computing environment corresponding to asynthesized voice authentication system 100 according to aspects of thisdisclosure. The synthesized voice authentication system 100 may includeone or more synthesized voice authentication devices (e.g., SVA node 11a, SVA node 11 b, SVA node 11 c, SVA node 11 n, and the like)communicatively coupled via one or more networks 115 to a biometriccombinatory device (BCD) that may be centrally located as a hubcoordinating operation of a plurality of SVA nodes. In some cases, thenetwork 115 may comprise one or more communications networks (e.g., theInternet, a telecommunications network, a wide area network (WAN), alocal area network (LAN), and the like). The biometric combinatorydevice 120 may be located on an organization's computing network (e.g.,network 125 that may comprise one or more of a WAN, LAN, or the like)and may be communicatively coupled to a plurality of business unitcomputing systems 140 and/or a biometric data repository 130 securelystoring biometric information received from one or more of the SVA nodes110 a-110 n, the BCD device 120, and/or the one or more business unitcomputing systems 140. In some cases, the biometric data repository maycomprise one or more data repositories storing biometric informationthat may be used as at least a portion of a user authorization and/orverification process. In some cases, the biometric data repository maybe located at a central location (e.g., an enterprise data repository ata data center), distributed over a number of servers, local to ageographic region, and/or the like. In an illustrative example, thebiometric data repository 130 may store biometric information such assynthesized voice authentication and verification information associatedwith a plurality of individuals, biometric information associated withthe plurality of individuals including facial recognition information,natural voice information, fingerprint information, and the like. Suchinformation, may be used with the synthesized voice authentication andverification information to provide a greater level of security,confidence and/or reliability that a received synthesized voiceauthentication request corresponds the intended user.

In some cases, the synthesized voice authentication system 100 mayinclude one or more devices that may be considered to be computingsystem comprising an obfuscated voice authentication engine. Thesynthesized voice authentication system 100 may be used to in thetransition from a standard voice authentication environment to asynthesized and/or hybrid voice authentication environment using amarkup language. In some cases, the markup language may be an adaptablemarkup language, where patterns may be identified over time andincorporated into the markup language to ensure greater security fromuse of the synthesized voice identifier and/or ensure processing occurswith optimized efficiency, such as by leveraging machine learning and/orartificial intelligence techniques. In a traditional voiceauthentication system, a voice pattern may be identified based on ananalysis of an audio sample of an individual's voice. For example, aperson may speak into a microphone to capture a signal representative ofthat individual's voice, such as to capture a recording of a particularphrase or sound pattern. This captured signal may be processed andanalyzed to create a record of a unique audio identifier that may beassociated with the particular individual. However, such methods may besubject to potential attempts to improperly bypass any security measuresusing that audio identifier, such as by another person mimicking theindividual's voice, using a captured recording of the individualspeaking, and/or the like.

By using the devices of the synthesized voice authentication system 100,an institution (e.g., a financial institution, a governmentorganization, an educational institution, or the like) may use one ormore devices processing algorithms specifically coded to performsynthetic voice authentication procedures based on a particular profile.In some cases, the synthesized voice authentication devices 110 a-100 nmay be used to generate a synthesized voice for the individualassociated with each particular synthesized voice authentication device110 a-100 n. In some cases, the synthesized voice authentication devices110 a-100 n may allow individuals with disabilities (e.g., a sightimpaired individual, a blind individual, an individual with a speechdisability, and the like.) to generate a unique synthesized voicepattern for use in authenticating an identity of the associatedindividual who, in some cases, may not be able to easily take advantageof traditional voice authentication procedures. In some cases,individuals that cannot speak, or who have difficulties in speaking, canleverage the synthesized voice for one or more voice recognitionpurposes.

In some cases, the biometric combinatory device 120 may be used as a“trusted” engine for use as a validation point for the plurality of SVAnodes 110 a-110 n and may, upon proper validation of a synthesizedvoice, issue a specific value, token, or the like. Unlike traditionalvoice authentication methods, this system may also be leveraged for usewith one or more different languages. In some cases, the synthesizedvoice authentication system 100 may be adapted to include and/or mergewith the capabilities of one or more different authenticationtechnologies, such as password protection and the like. In some cases,the synthesized voice authentication signal may be combined withtraditional or adaptive encryption schemes on the different combinatorypoints, such as at a particular synthesized voice authentication node(e.g., the SVA node 110 a or the like) to enhance security provided byusing the synthesized voice authentication system 100. For example, theBCD 120 may receive a synthesized voice signal from the SVA node 110 acorresponding to a user of the SVA node 110 a. The BCD 120 may, or mayfurther cause the SVA node 110 a, to include additional useridentification information (e.g., biometric information, passwordinformation, address information, geolocation information, and the like)that may be stored in the biometric data repository 130 and/or may beretrieved form one or more of the connected business unit computingsystems 140. Such information, along with geolocation information of theSVA node, may be used to include an audio signal having a randomizedfrequency (e.g., at an audible frequency, at an inaudible frequency,and/or the like), along with at least a portion of the user's audiocapture, in the generated synthesized voice signal.

In some cases, the SVA 110 a may be configured to provide feedbackregarding a status of a voice authentication process, to the user. Insome cases, the SVA device 110 a may include a visual indicator device,an audio indicator device, a text-based indicator, haptic feedback,and/or the like.

In some cases, the synthesized voice authentication system 100 may beused for one or more different network security purposes with respect tothe organization providing this functionality. For example, thesynthesized voice signal, the SVA nodes, the BCD 120 may be leveraged toenter secure locations in branches, offices, labs, and the like. In anillustrative example, the synthesized voice authentication system 100may include Point-to-Point connection authentication, e.g., anapplication to beacon authorization process, as a functionality providedfor with the use of the voice authentication signal. In such cases, anindividual who has requested access to a system may have a synthesizedvoice signal based on an audio sample provided by the individual,examined to determine whether an existing profile has been identified inthe system. In doing so, the synthesized voice authentication system 100may increase the reliability of the security and verification proceduresof the organization and may provide additional protection againstindividuals having one or more false profile by ensuring that a storedvoice authentication profile may uniquely identify a user and eliminate,or at least minimize, exposure to falsely created profiles. In somecases, some or all of the generation of the synthesized voice signaturemay be generated on the SVA node 110 a, the biometric combinatory device120 or a combination of processing split between the devices. In somecases, the operation of the SVA 110 a, the BCD 120 may be adapted due tooperation of slow speed Internet devices and/or slow networkconnections.

FIG. 2 shows an illustrative synthesized voice authentication (SVA)device 210, which may correspond to SVA device 110 a, SVA device 110 b,SVA device 110 c, and SVA device 110 d, according to aspects of thisdisclosure. The illustrative SVA device 210 may include a processor 212,a non-transitory memory device 214, a user interface device, acommunications interface that may be used to communicatively couple theSVA device 210 to one or more networks (e.g., the network 115), and oneor more input/output (I/O) devices 217. The SVA device 210 may alsoinclude one or more sensors for use in capturing or otherwise sensingbiometric information of a user of the device 210. For example, the SVAdevice 210 may include one or more sensors within the device and one ormore sensors externally connected to the SVA device 210, such as thebiometric sensors 220 which may be communicatively coupled to the SVAdevice 210 via an I/O port. The SVA device 210 may further include oneor more of an audio input 230 (e.g., a microphone), a fingerprint sensor240, a camera 250 (e.g., a still camera, a video camera, and the like),a location sensor (e.g., a GPS device, a triangulation device such as atelecommunications modem, and the like).

In some cases, the SVA device 210 may be a stand-alone device dedicatedto the function of generating a synthesized voice for an individual. Insome cases, the SVD 210 may be implemented as an application and/or anapplication programming interface (API) interfacing with an applicationrunning on a personal device, such as a personal mobile device (e.g., amobile phone, a wearable device, a smartwatch, and the like), a laptopcomputer, a desktop computer, a tablet device, and the like. In somecases, the SVA device 210 may be a stand-alone device, such as aself-service transaction device, an automated teller machine (ATM), akiosk, or the like. In some cases, the SVA device may be an externalhardware device that may plug into, or otherwise connect, with apersonal computing device. For example, the SVA 210 may connect to amobile phone, or other such device, as a “dongle” via a hardwiredconnection (e.g., a universal serial bus (USB) connection, a mini-USBconnection, and the like) or via a wireless connection (e.g., abluetooth connection, a WiFi, connection, a telecommunications networkconnection, an infrared (IR) communication link, and the like).

In some cases, the SVA device 210 may communicate, via the network 115with the BCD device 120 using a defined communications protocol and/or aspecified markup language to perform handshaking with the device, suchthat the SVA 210 may be used as an authentication device for theassociated user. In some cases, an individual, may desire to use the SVA210 to generate a synthesized voice identifier that may be used for datasecurity and/or authentication purposes. In some cases, the individualmay have a disability and may desire to use the synthesized voiceidentifier to allow use of the added security that the synthesized voiceidentifier offers. In some cases, the individual may desire to use thesynthesized voice identifier to avoid a possibility of an improperimpersonation used as an attempt at improper activity at the expense ofthe individual's credentials. By combining the user's voice signal withadditional information that may be incorporated into the voice signal,and/or applied as an overlay over the user's voice signal as, for anexample, a “digital watermark”. In doing so, the synthetic voiceinformation may be used to avoid, and minimize the risk ofeffectiveness, of another person attempting use of mimicry, animpression, and/or vocal recordings in bypassing voice authenticationmeasures to access private user information on a network, performingimproper financial activity using the user's credentials, and/or thelike.

In some cases, the user's voice signal may be captured using the audioinput 230 and comprise a sample of the user speaking, such as speaking aparticular word or phrase. The SVA may capture a geographic locationusing the location sensor 260 to identify a current location of the userand the SVA device 210. Additionally, the SVA device may captureadditional biometric information using one or more biometric sensors,such as the fingerprint sensor 240, the camera 250, and other suchsensor that may be capable of capturing biometric information. In somecases, the biometric information captured may be used in a userauthentication procedure for allowing the user access to the SVA device.For example, the SVA device 210 may capture fingerprint information,retina scan information, facial recognition information with or withouttext information, or other user verification and/or passwords tofacilitate user logon to the SVA device and/or an SVA applicationrunning on the SVA device 210. Once the user login has been verified,the user may be allowed access to one or more features and/orfunctionality of the SVA device, including creation and/or use of asynthetic voice identifier. In some cases, the SVA device may beconfigured to incorporate additional information into the captureduser's voice signal to generate a unique synthetic voice identifier forthe user. In some cases, the synthetic voice identifier may sound like anormal human voice to human ears. To accomplish this, additional audioinformation having a different audio frequency may be incorporated intothe captured voice signal, that can be used as a “digital audiofingerprint” unique to a particular user. Such frequencies may beselected from a range of frequencies inaudible (e.g., a sub-audiblerange of frequencies and the like) to the human ear, and/or at powerlevels that may be heard by a human as being similar to “background”noise in relation the captured voice signal, if heard at all. Forexample, the synthesized voice identifier may not cause the user's voiceto sound artificial (e.g., like a robot). Instead, signals at a desiredpower level and frequency may be added to the captured voice signal as asub-audible frequency. However, these added frequencies may act as adigital “fingerprint” for the user's voice. In some cases, the signaloverlay may be tracked on recording by storing an electronic code in thebackground.

FIG. 3 shows an illustrative biometric combinatory device 320 accordingto aspects of this disclosure. The BCD 320 may be a stand-alone server,a server cluster, and/or a distributed computer network. The BCD 320 mayinclude a processor 322, a memory device 324, a communications interface328, a user interface 326, one or more user interface screens 327, oneor more I/O devices 327 (e.g., a stylus, a keyboard, a touchscreen, andthe like), and a data repository that may be used to store synthesizedvoice identifiers associated with a plurality of the users. The datarepository may include biometric information associated with each user.In some case, the BCD 320 may be communicatively coupled to the SVAnodes via the communication interface 328 and the network 115 and to theplurality of business unit computing systems 140 and one or more datarepositories, such as the biometric information data repository 130. Insome cases, the BCD 320 may include an API 329 that may be used tofacilitate, with the SVA nodes 110 a-n, synthetic voice identifiercreation and use to authenticate a user's identity in a number ofapplication on an organizations network. For example, the BCD mayinclude one or more software modules including an aggregation engine330, a translation module 340, a collection module 350, an integrationmodule 360, and a validation module 370. In some cases, the BCD 320 mayinclude a firewall to further isolate the BCD 320 form unwanted externalincursion or interferences

In some cases, the aggregation engine 330 may be implemented in one ormore programming languages, such as C or an object-oriented programminglanguage, such as JAVA, C++, or the like. The aggregation engine 330 maybe a biometric information aggregation module of an API that aggregates,analyzes and/or reports biometric activities for the businessorganization network with a markup language. In some cases, theaggregation engine 330 may process zoned or un-zoned data analytics incollecting biometric information from one or more devices active on thenetwork (e.g., the SVA 210), from one or more business unit computingsystems 140, and/or may retrieve or store biometric information in thebiometric information data repository 130, such as by using one or moreAPI functionality. The aggregation engine 330 may also be implemented as“middleware” to allow different applications running on theorganization's computing systems to leverage biometric and syntheticvoice identifier information in their user authentication procedures.For example, the BCD 320 may be implemented on a central server at adata center, where one or more of the business unit computing systems140 may send or receive a synthetic voice identifier for verification aspart of a user authentication procedure. In an illustrative example, thesynthetic voice identifier information may be stored in a block chainconfiguration for each user, to allow for data segmentation and managingthe user information with respect to one or more different accessprofiles. In some cases, the aggregation engine 330 may be configured toanalyze received voice information to translate to or from differentlanguages, a text to speech format, a speech to text format, and thelike. For example, by using parametric text to speech technologies, theaggregation engine 330 may be capable of isolating and transformingcomponents with a speech data element. The aggregation engine 330 mayalso perform compression via a number of lossless data compressionmethods, such as a Lempel-Ziv compression method, to minimize an amountof data transferred when using the biometric information (e.g.,synthetic voice identifier, fingerprint information, retina scaninformation, and the like) for user authentication, while losing minimaldata resolution, when communicating the biometric information betweennetwork components, such as SVA nodes, data repositories, business unitcomputing systems and the like.

In some cases, the aggregation engine 330 may exchange data in near realtime with the SVA nodes, the business unit computing systems 140 and/orthe biometric information data repository 130. In addition to biometricinformation (e.g., voice information, synthetic voice information,retinal scan information, facial recognition information, fingerprintinformation, and the like), the aggregation engine may also communicateassociated information such as a user name, user identifier, a useraddress, a connection identifier, a geographic location associated withthe user, and the like. In an illustrative application, a business unitcomputing system may leverage fingerprint information when a userrequests certain actions, such as opening a large account acrossborders. In such cases, transactions such as these may face increasedscrutiny to avoid and disprove suspicious or improper activities bypersons not authorized to take such actions. Certain biometricidentifiers may be used for authentication purposes, such as forfingerprint verification. However, these authentication purposes may becircumvented in some cases, such as, by utilizing a synthetic voiceidentifier that may include additional frequency components randomlygenerated based on the authorized user's information (e.g., a knownlocation of the user, fingerprint data of the user, and the like), theopportunities for improper activity to succeed are minimized if noteliminated in most cases.

The aggregation engine 330 of the biometric combinatory device 320 maybe implemented in a server and utilize a markup language forcommunication and translation, in near real-time, of real-time and/orhistorical biometric information to different system components via aserver-based or web-based application interface. In some cases, thetranslation module 340 be used to adapt interpreted language for use inapplication-to-application functionality, including during initialhandshaking. The translation module 340 may also be used to facilitatedata analytics based on structured presentation, via a markup language,specifically tailored for use in communicating biometric and/orsynthesized voice information associated with users. In some cases,biometric information, such as the synthesized voice information, may beenhanced with additional data that may be associated with a same userand stored in the biometric data repository 130 and/or other datarepositories. Additionally, the markup language (e.g., a proprietarymarkup language, and the like) may be configured to support multiplelanguages with single and/or multiple byte translation to reduceproblems that may arise based on multi-language access. In some cases,this support may include translation of information in a first languageto corresponding information in a second language.

In some cases, the translation module 340 may be configured to supportsingle byte input/output data and/or double byte input/output data. Inan illustrative example, the translation module 340 may use aproprietary markup language to provide a nuanced application forsynthesized voice recognition, authentication, and verificationapplications. The markup language may be used to facilitate storageand/or retrieval of biometric information, including synthesized voiceinformation in an organized manner. For example, the markup language maybe used to format storage and communication of combined biometric anduser information including identifier information (e.g., useridentifier, user name, time, date, geographic location, networklocation, device identifier, phone number, address, email address, andthe like). Additional markup language entries may correspond to a typeor combination of types of biometric information used, including naturalvoice information, synthesized voice information, fingerprintinformation, retina scan information, facial recognition information,and/or the like. Biometric data communicated may be defined in themarkup language as whether the biometric data is being transferred in acompressed or uncompressed format. The markup language may also define alocation in the communication packets at which the biometric data can beread (e.g., a data container), including handshaking and control words,status words, data container size and/or length, packet count, checksum,and/or the like. In some cases, the markup language may have an entrycorresponding to whether the biometric information is historicalinformation, real-time (or near real-time information), or the like. Insome cases, the markup-language may be used to define one or moresecurity methods and/or protocols that may be used in the communicationof the biometric information.

In some cases, the biometric combinatory device 320 may include anintegration module 360 for use in integrating additional information(e.g., biometric information, rotating frequency information,geolocation information, and the like) with a natural voice signal togenerate a synthesized voice authentication identifier for each of aplurality of users. In some cases, the integration module may performthe integration functionality at a central location (e.g., at a BCDdevice server, and the like), at distributed locations (e.g., at one ormore regional BCD device servers based on geolocation information,business unit information and the like), and/or at remote locations,such as at an SVA node 210. In an illustrative example, the integrationmodule 360 may be configured to receive a voice signal associated with auser and additional information corresponding to the user. In somecases, an SVA node may capture a voice signal (e.g., in response to auser input) associated with a user and may communicate the capturedvoice signal to the BCD device 320 with additional information includingone or more of a user name, user identifier, a geographic locationassociated with the use (e.g., a current location, a residence location,and the like), a device identifier of the SVA node, capturing theinformation, and the like). The integration module may then determine arandomized frequency for inclusion with the captured natural voicesignal based on at least the additional information received from theSVA node 210 and/or user associated information (e.g., user fingerprintbiometric information, user retina scan biometric information, and thelike) retrieved from a data repository, such as the biometricinformation data repository 130. In an illustrative example, arandomized frequency may be obtained using a number returned from one ofa pseudo-random number generator or a true random number generator,where a seed number may correspond to one of a base frequency in asub-audible range and/or a number associated with biometric information,geolocation information, and/or the like. In an illustrative userverification application, a true random number generator may be used,based on a geolocation of the user device. For example, a latitudevalue, a longitude value, an address number associated with a latitudeand longitude value received from the SVA node 210 may be used tocalculate a true random number to be used in determining the frequencyand/or amplitude of an additional signal to enhance the natural voicesignal to generate a synthesized voice signal associated with the user.For example, the random number Rnum may be generated by the formulaRnum=RND(GeoVal), where RND( ) is a true random number generatorfunction and GeoVal is a value associated with the geolocation of theuser, such as a latitude, a longitude, a value combining the latitudeand longitude of the users location, and/or the like. To determine thefrequency, this random number may be used as a variable in a frequencydetermination function. For example, the frequency F may be determinedusing a formula, such as F=Fbase*(Rnum.*a), where Fbase is apredetermined sub-audible frequency, Rnum is the calculated randomnumber and a is a scalar value for scaling the random number to ensure Fremains within a predetermined range. Please note that the equations areshown for illustrative purposes and other such formulas and/or equationsmay be used.

In many situations, the frequency determination may be done a singletime for a communications session. In some cases, a rotating frequencymay be used by the integration module 360, where the rotating frequencymay be determined at a first time associated with a user action, such aswhen a user attempts to log into an account. In some cases, such asduring communications requiring a higher security level, the rotatingfrequency may change during the duration of the communications while theuser is logged into the account. In some cases, the changing of therotating frequency may be done during a session as a checkpoint duringthe session to ensure a same individual that logged into the account isparticipating in the session.

In some cases, a collection module 350 may be configured to collectinput processed by the BCD 320. For example, the collection module 350may identify a user and/or a business unit associated with the inputdata and aggregate the data associated with each user and/or businessunit individually. In some cases, the collection module may becustomized by a user or administrator to generate a report for useinternally by the organization, reporting to a central agency, and/orreporting to a government agency when laws, rules and/or regulationrequire reporting profile based data. In some cases, the collectionmodule may be used to log off each synthesized voice activity forauditing, non-repeating check and other compliance directives.

In an illustrative example, the components of the synthesized voiceauthentication system 100 may be used to create and user synthesizedvoice information for authentication and verification processes to helpguarantee data security and/or minimize suspicious and improper activityassociated with user accounts. The SVA nodes 110 a-110 n and the BCD 120may, with additional information provided by the biometric datarepository 130 and/or the business unit computing systems 140, may beused to generate a synthesized voice for one or more users. In somecases, the SVA 110 may capture a user's voice in an audio clip and theadditional information may be encoded into the audio clip, such as bycreating a digital audio watermark, to create a unique identificationfor a particular user. In some cases, data may be encoded in real timeor near real-time. In cases of suspected suspicious activity and/orimproper activity, the synthesized voice identifier used by a user maybe analyzed, such as by the BCD 120, to verify the user's identity andor validate the authentication information. In an illustrative example,an individual may attempt to set up a large (e.g., greater than$100,000) commercial loan. The synthesized voice identifier provided bythe user may be analyzed by the BCD 120 to ensure that the synthesizedvoice identifier has not already been associated with anotherindividual. If so, the transaction and/or user may be identified forfurther examination and/or may be reported to local law enforcement forinvestigation. In some cases, real-time vocal events may be communicatedin near real-time to ensure a same individual has continued in thetransaction. Such audio communications may be recorded and stored for apredetermined time period. In this way, audio signals may be used toensure a unique individual is associated with a unique synthesized voiceidentifier, even those who normally due to disabilities or otherreasons, are not capable of using traditional voice recognitiontechniques. In some cases, a synthesized voice identifier may be pairedwith other biometric identifiers to ensure greater security, such as byusing retina scan information, facial recognition information,fingerprint information and/or the like.

FIG. 4 shows an illustrative method 400 for generating a synthesizedvoice authentication signal according to aspects of this disclosure. At410 a synthetic voice authorization (SVA) device, such as the SVA nodes110 of FIG. 1 may be configured to capture an audio signal of a voice ofthe user of the SVA device. In some cases, the SVA device 110 maycapture a real-time, or near real-time, voice signal. In some cases, theSVA device 110 may capture a recorded audio signal, such as an audioclip or sample having a set duration. In some cases, the SVA node maycapture the audio signal in response to an input received via an inputdevice at the SVA node 110. In some cases, the SVA node may receive arequest to capture an audio input signal from the user in response to arequest received via the network 115, based on the user attempting to anactivity requiring authentication of user credentials and/or secure datahandling based on the requested activity. In some cases, at least aportion of the audio sample may be saved in a data repository local tothe SVA node 110 and/or at a centralized data repository, such as thedata repository 130.

At 420, the captured audio signal may be modified based on randomizedbiometric information in response to additional information captured andassociated with the user. In some cases, the SVA node, when capturing anaudio sample of the user, may capture other information, such aslocation information (e.g., a geographic location of the user and/or theSVA device 110 capture using the location sensor 260), biometricinformation (e.g., a retinal scan and/or a facial recognition scan usingthe camera 250, fingerprint information captured using the fingerprintsensor 240 and/or the like. In some cases, the SVA device 110 mayanalyze at least a portion of the captured information locally and/ormay communicate the captured information via the network 115 to thebiometric combinatory device 120 for analysis and/or modification. Insome cases, the geolocation information, the biometric informationand/or other information may be analyzed and used in calculations togenerate information used to modify the captured audio signal (e.g., anatural voice signal). In some cases, the BCD 120 may analyzegeolocation information to generate a randomized frequency orcombination of frequencies, to be included in or superimposed upon thenatural voice signal. In such cases, the randomized frequency may beinaudible to humans and may provide little to no distortion to thenatural voice signal, but may be found based on, for example, afrequency analysis performed on the synthesized voice signal. In somecases, the SVA 110 may process the information locally to generate the“audio watermark” to be superimposed on the audio signal. In some cases,the BCD device may process the information at a central location andcommunicate the determined digital watermark to the SVA 110 forinclusion into the audio watermark in the synthesized voice identifierassociated with that particular user. In some cases, such as forindividuals with disabilities and/or difficulty with speaking, the audiosample may be machine generated audio, or in some cases, a modified textto speech format.

At 430, the SVA 110 may use the audio watermark to modify the capturedvoice signal to generate a synthesized voice signal associated with theuser. The SVA 110 may superimpose the audio watermark, or other suchfrequency based signal, onto the captured voice signal. In some cases,the audio watermark may be based on geolocation information, or othersuch user related information.

At 440, the SVA 110 may generate a synthesized voice identifier, for useby the user, based on the combined audio watermark and the capturedaudio signal. In some cases, the SVA 110 may generate the synthesizedvoice identifier based, at least in part, on geolocation informationcaptured by the SVA device, and/or other such information (e.g.,biometric information and the like).

FIG. 5 shows an illustrative method 500 for aggregating and analyzingbiometric and synthesized voice authentication signals according toaspects of this disclosure. At 510, a collection module of the BCD 120may aggregate synthesized voice information from a plurality of SVAnodes, wherein each synthesized voice identifier is associated with aparticular user. At 520, an integration module may aggregate biometricinformation and/or geographic information associated with eachparticular user from a plurality of sources including the VCA 110,biometric information data repositories, and/or business unit computingsystems. At 530, a markup language translation module may configure asynthesized voice signal for an SVA node based on a randomized frequencydetermined from geolocation information and/or biometric information andreceive at an aggregation engine, one or more synthesized voice signalsfrom a plurality of SVA nodes, in response to a user request foraccessing secured information and/or to perform an action requiringproper authentication.

One or more aspects of the disclosure may be embodied in computer-usabledata or computer-executable instructions, such as in one or more programmodules, executed by one or more computers or other devices to performthe operations described herein. Generally, program modules includeroutines, programs, objects, components, data structures, and the likethat perform particular tasks or implement particular abstract datatypes when executed by one or more processors in a computer or otherdata processing device. The computer-executable instructions may bestored as computer-readable instructions on a computer-readable mediumsuch as a hard disk, optical disk, removable storage media, solid-statememory, RAM, and the like. The functionality of the program modules maybe combined or distributed as desired in various embodiments. Inaddition, the functionality may be embodied in whole or in part infirmware or hardware equivalents, such as integrated circuits,application-specific integrated circuits (ASICs), field programmablegate arrays (FPGA), and the like. Particular data structures may be usedto more effectively implement one or more aspects of the disclosure, andsuch data structures are contemplated to be within the scope of computerexecutable instructions and computer-usable data described herein.

Various aspects described herein may be embodied as a method, anapparatus, or as one or more computer-readable media storingcomputer-executable instructions. Accordingly, those aspects may takethe form of an entirely hardware embodiment, an entirely softwareembodiment, an entirely firmware embodiment, or an embodiment combiningsoftware, hardware, and firmware aspects in any combination. Inaddition, various signals representing data or events as describedherein may be transferred between a source and a destination in the formof light or electromagnetic waves traveling through signal-conductingmedia such as metal wires, optical fibers, or wireless transmissionmedia (e.g., air or space). In general, the one or morecomputer-readable media may be and/or include one or more non-transitorycomputer-readable media.

As described herein, the various methods and acts may be operativeacross one or more computing servers and one or more networks. Thefunctionality may be distributed in any manner, or may be located in asingle computing device (e.g., a server, a client computer, and thelike). For example, in alternative embodiments, one or more of thecomputing platforms discussed above may be combined into a singlecomputing platform, and the various functions of each computing platformmay be performed by the single computing platform. In such arrangements,any and/or all of the above-discussed communications between computingplatforms may correspond to data being accessed, moved, modified,updated, and/or otherwise used by the single computing platform.Additionally, or alternatively, one or more of the computing platformsdiscussed above may be implemented in one or more virtual machines thatare provided by one or more physical computing devices. In sucharrangements, the various functions of each computing platform may beperformed by the one or more virtual machines, and any and/or all of theabove-discussed communications between computing platforms maycorrespond to data being accessed, moved, modified, updated, and/orotherwise used by the one or more virtual machines.

Aspects of the disclosure have been described in terms of illustrativeembodiments thereof. Numerous other embodiments, modifications, andvariations within the scope and spirit of the appended claims will occurto persons of ordinary skill in the art from a review of thisdisclosure. For example, one or more of the steps depicted in theillustrative figures may be performed in other than the recited order,and one or more depicted steps may be optional in accordance withaspects of the disclosure.

What is claimed is:
 1. A system comprising: a biometric combinatorydevice, comprising: a first processor; and a non-transitory memorystoring first instructions that, when executed by the processor, causethe biometric combinatory device to: receive, at a collection modulefrom a synthesized voice authentication device, geographic locationinformation and biometric information associated with a user, whereinthe geographic location information corresponds to a geographic locationof the user captured by a location sensor of the synthesized voiceauthentication device and the biometric information comprises an audioclip of a voice of the user captured during an authentication request;generate a synthesized voice identifier associated with the user as acombination of a randomized audio frequency signal based on thegeographic location information and the audio clip; and communicate, viaa network connection, the randomized audio frequency signal to thesynthesized voice authentication device; and the synthesized voiceauthentication device comprising: a second processor; and anon-transitory memory storing second instructions, that when executed bythe second processor, cause the synthesized voice authentication deviceto: capture an audio signal of a voice of the user; generate asynthesized voice identifier associated with the user based on the audiosignal and the randomized audio frequency signal; and communicate, to aremote device, the synthesized voice identifier during a userauthentication process.
 2. The system of claim 1, comprising the remotedevice, wherein the remote device stores non-public informationcorresponding to an activity requested by the user; and wherein thesecond instructions, when executed by the second processor, cause thesynthesized voice authentication device to: receive, from the remotedevice, a request for authentication of a current user, wherein thesynthesized voice identifier is communicated based on the request forauthentication of the current user.
 3. The system of claim 1, whereinthe audio signal is captured as the user speaks a particular soundpattern.
 4. The system of claim 1, wherein the audio signal is capturedas a recorded audio clip.
 5. The system of claim 1, wherein thesynthesized voice authentication device comprises a geolocation deviceand wherein the second instructions when executed by the secondprocessor, cause the synthesized voice authentication device to:capture, by the geolocation device, geolocation information concurrentlywith capturing the audio signal.
 6. The system of claim 1, wherein thesynthesized voice authentication device comprises a personal mobiledevice operating a synthetic voice authorization application.
 7. Thesystem of claim 1, wherein the biometric combinatory device comprises acentrally located computing system communicatively coupled to aplurality of remote computing systems running applications receiving thesynthesized voice identifier for use in an authentication process. 8.The system of claim 1, wherein the synthesized voice authenticationdevice comprises a personal mobile device including an input port and ahardware device operating a synthetic voice authorization applicationplugged into the input port.
 9. The system of claim 1, wherein thesecond instructions, when executed by the second processor, cause thesynthesized voice authentication device to: capture, via an inputdevice, a text signal corresponding to a text to speech signalassociated with the user; and generate, based on the text to speechsignal, the audio signal of the voice of the user as a machine-generatedaudio signal.
 10. The system of claim 1, wherein the randomized audiofrequency signal comprises a signal at a frequency within a sub-audiblerange.
 11. The system of claim 1, wherein communication of thesynthesized voice identifier includes multi-language support.
 12. Amethod comprising: receiving, by a biometric combinatory device,geographic location information, an audio clip of a user, and biometricinformation captured by a device associated with the user; generating arandomized audio frequency signal based on the geographic locationinformation, the audio clip, and the biometric information;communicating, via a network connection, the randomized audio frequencysignal to a synthesized voice authentication device; generating, by asynthesized voice authentication device, a synthesized voice identifierassociated with the user based on a captured audio signal and therandomized audio frequency signal, wherein the audio signal comprises avoice of the user captured by the synthesized voice authenticationdevice; and sending, to a remote device, the synthesized voiceidentifier during a user authentication process.
 13. The method of claim12, wherein the audio signal is captured by the synthesized voiceauthentication device and wherein the user authentication processcomprises multi-language support.
 14. The method of claim 12, whereinthe randomized audio frequency signal comprises a signal at a frequencywithin a sub-audible range.
 15. The method of claim 12, comprising:capturing, via an input device, a text entered by the user; andgenerating, based on the text, the audio signal as a machine-generatedvoice audio signal.
 16. The method of claim 12, wherein the synthesizedvoice authentication device comprises a user device including an inputport and a hardware device operating a synthetic voice authorizationapplication plugged into the input port.
 17. The method of claim 12,wherein the synthesized voice authentication device comprises a personalmobile device operating a synthetic voice authorization application. 18.The method of claim 12, wherein the biometric combinatory devicecomprises a centrally located computing system communicatively coupledto a plurality of remote computing systems running applicationsreceiving the synthesized voice identifier for use in an authenticationprocess.
 19. The method of claim 12, comprising: capturing thegeographic location information and the biometric informationconcurrently with the audio signal.
 20. The method of claim 12, whereinthe audio signal is captured as the user speaks a particular soundpattern.